Exascale workloads are expected to incorporate data-intensive processing in close
coordination with traditional physics simulations. These emerging scientific, data-analytics …

Crash consistency using persistent memory programming libraries requires programmers to
use complex transactions and manual annotations. In contrast, the failure-atomic msync …

The exponential growth in dataset sizes necessitates the use of high-performance
computing (HPC) for large-scale data science. Furthermore, the sizes of these datasets shift …

Demands on computational performance, power efficiency, data transfer, resource capacity,
and resilience for next generation high performance computing (HPC) systems present a …

Advancements in data acquisition technologies across different domains, from genome
sequencing to satellite and telescope imaging to large-scale physics simulations, are …

The Python programming language has become a popular choice for data scientists. While
easy to use, the Python language is not well suited to drive data science on large-scale …

Parameters of machinery parts have dependency relationships between each other. Given a
part, our task is to compute all the combinations of all its parameters constrained by the …

Because of the evolution of compute units, memory heterogeneity is becoming popular in
HPC systems. But dealing with such various memory levels often requires different …

A graph is an excellent way of representing relationships among entities. We can use graph
analytics to synthesize and analyze such relational data, and extract relevant features that …

Exascale workloads are expected to incorporate data-intensive processing in close
coordination with traditional physics simulations. These emerging scientific, data-analytics …